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Cyclic AMP-dependent plasticity underlies rapid changes in odor coding associated with reward learning.

ABSTRACT: Learning and memory rely on dopamine and downstream cAMP-dependent plasticity across diverse organisms. Despite the central role of cAMP signaling, it is not known how cAMP-dependent plasticity drives coherent changes in neuronal physiology that encode the memory trace, or engram. In Drosophila, the mushroom body (MB) is critically involved in olfactory classical conditioning, and cAMP signaling molecules are necessary and sufficient for normal memory in intrinsic MB neurons. To evaluate the role of cAMP-dependent plasticity in learning, we examined how cAMP manipulations and olfactory classical conditioning modulate olfactory responses in the MB with in vivo imaging. Elevating cAMP pharmacologically or optogenetically produced plasticity in MB neurons, altering their responses to odorants. Odor-evoked Ca²⁺ responses showed net facilitation across anatomical regions. At the single-cell level, neurons exhibited heterogeneous responses to cAMP elevation, suggesting that cAMP drives plasticity to discrete subsets of MB neurons. Olfactory appetitive conditioning enhanced MB odor responses, mimicking the cAMP-dependent plasticity in directionality and magnitude. Elevating cAMP to equivalent levels as appetitive conditioning also produced plasticity, suggesting that the cAMP generated during conditioning affects odor-evoked responses in the MB. Finally, we found that this plasticity was dependent on the Rutabaga type I adenylyl cyclase, linking cAMP-dependent plasticity to behavioral modification. Overall, these data demonstrate that learning produces robust cAMP-dependent plasticity in intrinsic MB neurons, which is biased toward naturalistic reward learning. This suggests that cAMP signaling may serve to modulate intrinsic MB responses toward salient stimuli.

SUBMITTER: Louis T

PROVIDER: S-EPMC5776964 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:At around 7 months of age, human infants begin to reliably produce well-formed syllables containing both consonants and vowels, a behavior called canonical babbling. Over subsequent months, the frequency of canonical babbling continues to increase. How the infant's nervous system supports the acquisition of this ability is unknown. Here we present a computational model that combines a spiking neural network, reinforcement-modulated spike-timing-dependent plasticity, and a human-like vocal tract to simulate the acquisition of canonical babbling. Like human infants, the model's frequency of canonical babbling gradually increases. The model is rewarded when it produces a sound that is more auditorily salient than sounds it has previously produced. This is consistent with data from human infants indicating that contingent adult responses shape infant behavior and with data from deaf and tracheostomized infants indicating that hearing, including hearing one's own vocalizations, is critical for canonical babbling development. Reward receipt increases the level of dopamine in the neural network. The neural network contains a reservoir with recurrent connections and two motor neuron groups, one agonist and one antagonist, which control the masseter and orbicularis oris muscles, promoting or inhibiting mouth closure. The model learns to increase the number of salient, syllabic sounds it produces by adjusting the base level of muscle activation and increasing their range of activity. Our results support the possibility that through dopamine-modulated spike-timing-dependent plasticity, the motor cortex learns to harness its natural oscillations in activity in order to produce syllabic sounds. It thus suggests that learning to produce rhythmic mouth movements for speech production may be supported by general cortical learning mechanisms. The model makes several testable predictions and has implications for our understanding not only of how syllabic vocalizations develop in infancy but also for our understanding of how they may have evolved.

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Cyclic AMP-dependent plasticity underlies rapid changes in odor coding associated with reward learning.

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